The present invention is related to drug discovery. Specifically, this invention provides methods useful for predicting drug interactions based upon the interaction between biological response profiles. The methods of the invention is also useful for analyzing the interaction between any perturbations to a biological system.
Simultaneous administration of several drugs, or combination therapy, is often necessary to achieve desired therapeutic objectives. For example, a heart failure patient may be treated with a diuretic in conjunction with a vasodilator and/or a cardiac glucoside so that the patient will have adequate cardiac output, but free from edema. In cancer chemotherapy or antimicrobial therapy, drug combination is desired to delay the emergence of drug resistant tumor cells or microorganisms. Nies, 1990, Principles of Therapeutics, In (Goodman and Gilman eds.) THE PHARMACOLOGICAL BASIS OF THERAPEUTICS. Recently, combination therapy has been successfully used in anti-viral therapies, such as in the suppression of replication of Human Immunodeficiency Virus (HIV).
Drug combination, while offering many benefits, often causes unintended adverse reactions because of undesirable drug interactions. Drug interaction is the ability of one drug to alter the effects of another. It may be beneficial or detrimental. See, http://des.sw.cc.va.us/nursing/DrugInter.html (accessed on Nov. 13, 1998). As an example of beneficial drug interaction effect, the combination of Demerol and Vistaril enhances the sedative effect of Demerol. Id. The combination of Aspirin and Coumadin, however, is detrimental because Coumadin increases the possibility of Aspirin induced bleeding. Id.
It is estimated that the incidence of drug interaction ranges from 3-40% in patients under combination therapy. Nies, 1990, Principles of Therapeutics, In (Goodman and Gilman eds.) THE PHARMACOLOGICAL BASIS OF THERAPEUTICS; Naguib et al., 1997, Clinically Significant Drug Interactions with General Anesthetics-incidence, Mechanisms and Management, MIDDLE EAST J. ANESTHESIOL,. 14:127-183. The frequency of drug interaction increases disproportionally with the increase in the number of drugs in combination. For example, only 5% of patients with fewer than six drugs manifested clinical signs of drug interaction; while 40% of patients given 16 drugs experienced an adverse drug interaction. Naguib et al.
Because most hospital patients receive at least six drugs in combination, drug interactions are of serious clinical concern. The problem of drug interaction is worsened by the growing population of geriatric patients who are often prescribed multiple medications for concomitant medical illnesses and who often have diminished capacity to metabolize drugs. Id. In fact, drug interactions have become the frequent causes of treatment failure and adverse reactions. Anastasio, et al., 1997, Drug Interactions: Keeping It Straight., AM. FAMILY PHYSICIAN 56:883. Steel et al., 1981, Iatrogenic Illness on a General Medical Service at a University Hospital. N. ENGL. J. MED. 304:638-642.
Because of the important clinical consequences of drug interactions, it is desirable to predict potential interactions of a drug candidate with other drugs or drug candidates in the early phase of drug development. Unfortunately, the ability to predict potentially dangerous interactions between drug treatments is lacking. Even at the late stage of drug development, such as at the stage of clinical trials, drug interactions are still particularly difficult to investigate because of the number of combinations that would have to be tested.
Discussion or citation of a reference herein shall not be construed as an admission that such reference is prior art to the present invention.
Accordingly, this invention provides methods suitable for predicting potential drug interaction in the early stage of drug development.
In one aspect of the invention, a plurality of cellular constituents (measurable biological variables, such as mRNA, proteins, etc., defined infra) are measured while a biological sample (either a model organism or a target subject) is separately subjected to the application of a perturbation A (such as a drug A) and a perturbation B (such as a drug B). The change in those cellular constituents (response of cellular constituents) are calculated, preferably as the log ratio of cellular constituent levels before and after treatment with either perturbation A or perturbation B.
A substantial overlap between the response of cellular constituents to perturbations A and B suggests that the two drugs will have a potential overlapping effect in vivo.
In preferred embodiments, the cellular constituents are mRNA transcripts. Perturbation interaction (such as drug interaction) is tested in a model organism such as a yeast culture.
In another aspect of the invention, a biological sample is subjected to the treatment of perturbation A (such as a drug A). The response (RA) of a number of cellular constituents is calculated based upon the level of cellular constituents before (baseline) and after treatment with perturbation A. The biological sample is then treated with perturbation A in the presence of perturbation B (such as a drug B). The response (RA|B) of the cellular constituents is calculated based upon the level of cellular constituents before (perturbation B only) and after (perturbations A and B) treatment with perturbation A.
The response profile RA is plotted against response profile RA|B with response of each cellular constituent as a data point. One axis represents response to drug A and another axis represents response to perturbation A in the presence of perturbation B. In one preferred embodiment, RA|B is plotted along a vertical axis and RA is plotted along a horizontal axis. In this embodiment, if a cellular constituent falls along the 45xc2x0 line, the activity of perturbation A on the cellular constituent is predicted to be independent of perturbation B. If a cellular constituent falls within the upper left or lower right quadrants, the activity of perturbation A on the cellular constituent is predicted to be dependent upon perturbation B. If a cellular constituent falls within the lower left or upper right quadrant, but not along the 45xc2x0 line, the activity of perturbation A on the cellular constituent is predicted to be either reduced or enhanced by perturbation B.
In some embodiments, the membership in the different regions is assigned with an objective statistical significance. The statistical significance is calculated based upon the error associated with each cellular constituent response measurement. In some instances, the error is derived from the variation between repeated measurements. In some other instances, the error is estimated based upon other error models.
In some other embodiments, interaction between perturbation A (such as a drug A) and perturbation B (such as a drug B) is calculated according to: {I}={RA,B}xe2x88x92({RA}+{RB}). Vector {I} is the interaction effect. Vector {RA} represents a set of cellular constituents with threshold crossing response when treating with perturbation A. Vector {RB} represents a set of cellular constituents with threshold crossing response when treating with perturbation B. Vector {RA,B} is a set of cellular constituents with threshold crossing response when treating with both perturbations A and B.
In yet another aspect of the invention, the methods of the invention are applied to analyze the interactions among any number of perturbations (such as any number of drugs). In some embodiments, the interaction between two groups of perturbations (groups A and B) are analyzed using the methods of the invention. In such embodiments, the response to group A is monitored as RA; the response to group B is the RB; and the response to group A in the presence of group B is RA|B.